Upon successful completion of this session, the participants will be able to:
learn the data analysis process cycle
develop a basic knowledge on various type of data analysis and its relevance
Data analysis is the process of exploring and analyzing datasets to find hidden patterns.
In Business applications the data size is large and demand more high level specific predictions to boost business. Tools for this specific tasks come under Data Analytics
Companies ideally need to use all of their generated data to derive value out of it and make impactful business decisions. Data analytics is used to drive this purpose.
R
Python
It will be an additional tool for effective utilization of your time
It’s possible to work as a data scientist using either Python or R
Python is more popular overall, but R dominates in some industries (particularly in academia and research)
Python Better than R for Data Science?Depends on nature of your work
Python skills would be more transferrable to other disciplines
Using Python and SQL, you write a query to pull the data you need from your company database.
Using Python and the pandas library, you clean and sort the data into a dataframe (table) that’s ready for analysis.
Using Python, with pandas and matplotlib libraries, you begin analyzing, exploring, and visualizing the data.
After learning more about the data through your exploration, you use Python with the scikit-learn library to build a predictive model that forecasts future outcomes for your company based on the data you pulled.
You arrange your final analysis and your model results into an appropriate format for communicating with your co-workers.
Diagnostic Analysis: Answers the question, “Why did this happen?” – Pattern identification
Predictive Analysis: Answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events.
Prescriptive Analysis: Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
Statistical Analysis: Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:
Descriptive: Explain “What the data says”
Inferential: Generalizes the observations from descriptive statistic by testing of hypothesis on samples
